Applications currently exist that analyze an image of an object to extract information about its texture. The extracted texture information may then be used by other applications. For example, the texture information serves as a low level descriptor for content-based indexing and retrieving. Content based indexing and retrieving is often used in several industries such the textile industry, tile industry, crystal industry and the like.
Current content-based retrieval techniques begin by analyzing photographic images of the object. Typically, a first level of analysis is performed manually by an operator. Such operators visually examine the image to determine the texture of the object. However, the determination of the texture is not very accurate. In addition, the operator may not accurately perceive a texture image that has undergone geometrical transformation such as rotation or scaling.
Numerous techniques have been developed to consider the geometric transformation of the image while extracting the texture information. Rotation invariant feature extraction is one such technique that takes into consideration the rotation of the image while extracting the feature of the texture. However, most rotation invariant feature extraction techniques are based on image rotation and do not take into account physical surface rotation of the image.
Surface rotation invariant techniques have been developed to address the surface rotation parameters related to image rotation. However, most surface rotation invariant techniques require at least three images for processing thereby increasing processing complexity and associated costs. In addition, in most cases three images may not be available for processing.